import os os.environ['HF_HUB_CACHE'] = './checkpoints/hf_cache' import time from subprocess import CalledProcessError from typing import Dict, List import torch import torchaudio from torch.nn.utils.rnn import pad_sequence from omegaconf import OmegaConf from tqdm import tqdm import warnings warnings.filterwarnings("ignore", category=FutureWarning) warnings.filterwarnings("ignore", category=UserWarning) from indextts.BigVGAN.models import BigVGAN as Generator from indextts.gpt.model import UnifiedVoice from indextts.utils.checkpoint import load_checkpoint from indextts.utils.feature_extractors import MelSpectrogramFeatures from indextts.utils.front import TextNormalizer, TextTokenizer class IndexTTS: def __init__( self, cfg_path="checkpoints/config.yaml", model_dir="checkpoints", is_fp16=True, device=None, use_cuda_kernel=None, ): """ Args: cfg_path (str): path to the config file. model_dir (str): path to the model directory. is_fp16 (bool): whether to use fp16. device (str): device to use (e.g., 'cuda:0', 'cpu'). If None, it will be set automatically based on the availability of CUDA or MPS. use_cuda_kernel (None | bool): whether to use BigVGan custom fused activation CUDA kernel, only for CUDA device. """ if device is not None: self.device = device self.is_fp16 = False if device == "cpu" else is_fp16 self.use_cuda_kernel = use_cuda_kernel is not None and use_cuda_kernel and device.startswith("cuda") elif torch.cuda.is_available(): self.device = "cuda:0" self.is_fp16 = is_fp16 self.use_cuda_kernel = use_cuda_kernel is None or use_cuda_kernel elif hasattr(torch, "mps") and torch.backends.mps.is_available(): self.device = "mps" self.is_fp16 = False # Use float16 on MPS is overhead than float32 self.use_cuda_kernel = False else: self.device = "cpu" self.is_fp16 = False self.use_cuda_kernel = False print(">> Be patient, it may take a while to run in CPU mode.") self.cfg = OmegaConf.load(cfg_path) self.model_dir = model_dir self.dtype = torch.float16 if self.is_fp16 else None self.stop_mel_token = self.cfg.gpt.stop_mel_token # Comment-off to load the VQ-VAE model for debugging tokenizer # https://github.com/index-tts/index-tts/issues/34 # # from indextts.vqvae.xtts_dvae import DiscreteVAE # self.dvae = DiscreteVAE(**self.cfg.vqvae) # self.dvae_path = os.path.join(self.model_dir, self.cfg.dvae_checkpoint) # load_checkpoint(self.dvae, self.dvae_path) # self.dvae = self.dvae.to(self.device) # if self.is_fp16: # self.dvae.eval().half() # else: # self.dvae.eval() # print(">> vqvae weights restored from:", self.dvae_path) self.gpt = UnifiedVoice(**self.cfg.gpt) self.gpt_path = os.path.join(self.model_dir, self.cfg.gpt_checkpoint) load_checkpoint(self.gpt, self.gpt_path) self.gpt = self.gpt.to(self.device) if self.is_fp16: self.gpt.eval().half() else: self.gpt.eval() print(">> GPT weights restored from:", self.gpt_path) if self.is_fp16: try: import deepspeed use_deepspeed = True except (ImportError, OSError, CalledProcessError) as e: use_deepspeed = False print(f">> DeepSpeed加载失败，回退到标准推理: {e}") self.gpt.post_init_gpt2_config(use_deepspeed=use_deepspeed, kv_cache=True, half=True) else: self.gpt.post_init_gpt2_config(use_deepspeed=False, kv_cache=False, half=False) if self.use_cuda_kernel: # preload the CUDA kernel for BigVGAN try: from indextts.BigVGAN.alias_free_activation.cuda import load anti_alias_activation_cuda = load.load() print(">> Preload custom CUDA kernel for BigVGAN", anti_alias_activation_cuda) except: print(">> Failed to load custom CUDA kernel for BigVGAN. Falling back to torch.") self.use_cuda_kernel = False self.bigvgan = Generator(self.cfg.bigvgan, use_cuda_kernel=self.use_cuda_kernel) self.bigvgan_path = os.path.join(self.model_dir, self.cfg.bigvgan_checkpoint) vocoder_dict = torch.load(self.bigvgan_path, map_location="cpu") self.bigvgan.load_state_dict(vocoder_dict["generator"]) self.bigvgan = self.bigvgan.to(self.device) # remove weight norm on eval mode self.bigvgan.remove_weight_norm() self.bigvgan.eval() print(">> bigvgan weights restored from:", self.bigvgan_path) self.bpe_path = os.path.join(self.model_dir, self.cfg.dataset["bpe_model"]) self.normalizer = TextNormalizer() self.normalizer.load() print(">> TextNormalizer loaded") self.tokenizer = TextTokenizer(self.bpe_path, self.normalizer) print(">> bpe model loaded from:", self.bpe_path) # 缓存参考音频mel： self.cache_audio_prompt = None self.cache_cond_mel = None # 进度引用显示（可选） self.gr_progress = None self.model_version = self.cfg.version if hasattr(self.cfg, "version") else None def remove_long_silence(self, codes: torch.Tensor, silent_token=52, max_consecutive=30): """ Shrink special tokens (silent_token and stop_mel_token) in codes codes: [B, T] """ code_lens = [] codes_list = [] device = codes.device dtype = codes.dtype isfix = False for i in range(0, codes.shape[0]): code = codes[i] if not torch.any(code == self.stop_mel_token).item(): len_ = code.size(0) else: stop_mel_idx = (code == self.stop_mel_token).nonzero(as_tuple=False) len_ = stop_mel_idx[0].item() if len(stop_mel_idx) > 0 else code.size(0) count = torch.sum(code == silent_token).item() if count > max_consecutive: # code = code.cpu().tolist() ncode_idx = [] n = 0 for k in range(len_): assert code[ k] != self.stop_mel_token, f"stop_mel_token {self.stop_mel_token} should be shrinked here" if code[k] != silent_token: ncode_idx.append(k) n = 0 elif code[k] == silent_token and n < 10: ncode_idx.append(k) n += 1 # if (k == 0 and code[k] == 52) or (code[k] == 52 and code[k-1] == 52): # n += 1 # new code len_ = len(ncode_idx) codes_list.append(code[ncode_idx]) isfix = True else: # shrink to len_ codes_list.append(code[:len_]) code_lens.append(len_) if isfix: if len(codes_list) > 1: codes = pad_sequence(codes_list, batch_first=True, padding_value=self.stop_mel_token) else: codes = codes_list[0].unsqueeze(0) else: # unchanged pass # clip codes to max length max_len = max(code_lens) if max_len < codes.shape[1]: codes = codes[:, :max_len] code_lens = torch.tensor(code_lens, dtype=torch.long, device=device) return codes, code_lens def bucket_sentences(self, sentences, bucket_max_size=4) -> List[List[Dict]]: """ Sentence data bucketing. if ``bucket_max_size=1``, return all sentences in one bucket. """ outputs: List[Dict] = [] for idx, sent in enumerate(sentences): outputs.append({"idx": idx, "sent": sent, "len": len(sent)}) if len(outputs) > bucket_max_size: # split sentences into buckets by sentence length buckets: List[List[Dict]] = [] factor = 1.5 last_bucket = None last_bucket_sent_len_median = 0 for sent in sorted(outputs, key=lambda x: x["len"]): current_sent_len = sent["len"] if current_sent_len == 0: print(">> skip empty sentence") continue if last_bucket is None \ or current_sent_len >= int(last_bucket_sent_len_median * factor) \ or len(last_bucket) >= bucket_max_size: # new bucket buckets.append([sent]) last_bucket = buckets[-1] last_bucket_sent_len_median = current_sent_len else: # current bucket can hold more sentences last_bucket.append(sent) # sorted mid = len(last_bucket) // 2 last_bucket_sent_len_median = last_bucket[mid]["len"] last_bucket = None # merge all buckets with size 1 out_buckets: List[List[Dict]] = [] only_ones: List[Dict] = [] for b in buckets: if len(b) == 1: only_ones.append(b[0]) else: out_buckets.append(b) if len(only_ones) > 0: # merge into previous buckets if possible # print("only_ones:", [(o["idx"], o["len"]) for o in only_ones]) for i in range(len(out_buckets)): b = out_buckets[i] if len(b) < bucket_max_size: b.append(only_ones.pop(0)) if len(only_ones) == 0: break # combined all remaining sized 1 buckets if len(only_ones) > 0: out_buckets.extend( [only_ones[i:i + bucket_max_size] for i in range(0, len(only_ones), bucket_max_size)]) return out_buckets return [outputs] def pad_tokens_cat(self, tokens: List[torch.Tensor]) -> torch.Tensor: if self.model_version and self.model_version >= 1.5: # 1.5版本以上，直接使用stop_text_token 右侧填充，填充到最大长度 # [1, N] -> [N,] tokens = [t.squeeze(0) for t in tokens] return pad_sequence(tokens, batch_first=True, padding_value=self.cfg.gpt.stop_text_token, padding_side="right") max_len = max(t.size(1) for t in tokens) outputs = [] for tensor in tokens: pad_len = max_len - tensor.size(1) if pad_len > 0: n = min(8, pad_len) tensor = torch.nn.functional.pad(tensor, (0, n), value=self.cfg.gpt.stop_text_token) tensor = torch.nn.functional.pad(tensor, (0, pad_len - n), value=self.cfg.gpt.start_text_token) tensor = tensor[:, :max_len] outputs.append(tensor) tokens = torch.cat(outputs, dim=0) return tokens def torch_empty_cache(self): try: if "cuda" in str(self.device): torch.cuda.empty_cache() elif "mps" in str(self.device): torch.mps.empty_cache() except Exception as e: pass def _set_gr_progress(self, value, desc): if self.gr_progress is not None: self.gr_progress(value, desc=desc) # 快速推理：对于“多句长文本”，可实现至少 2~10 倍以上的速度提升~ （First modified by sunnyboxs 2025-04-16） def infer_fast(self, audio_prompt, text, output_path, verbose=False, max_text_tokens_per_sentence=100, sentences_bucket_max_size=4, **generation_kwargs): """ Args: ``max_text_tokens_per_sentence``: 分句的最大token数，默认``100``，可以根据GPU硬件情况调整 - 越小，batch 越多，推理速度越*快*，占用内存更多，可能影响质量 - 越大，batch 越少，推理速度越*慢*，占用内存和质量更接近于非快速推理 ``sentences_bucket_max_size``: 分句分桶的最大容量，默认``4``，可以根据GPU内存调整 - 越大，bucket数量越少，batch越多，推理速度越*快*，占用内存更多，可能影响质量 - 越小，bucket数量越多，batch越少，推理速度越*慢*，占用内存和质量更接近于非快速推理 """ print(">> start fast inference...") self._set_gr_progress(0, "start fast inference...") if verbose: print(f"origin text:{text}") start_time = time.perf_counter() # 如果参考音频改变了，才需要重新生成 cond_mel, 提升速度 if self.cache_cond_mel is None or self.cache_audio_prompt != audio_prompt: audio, sr = torchaudio.load(audio_prompt) audio = torch.mean(audio, dim=0, keepdim=True) if audio.shape[0] > 1: audio = audio[0].unsqueeze(0) audio = torchaudio.transforms.Resample(sr, 24000)(audio) cond_mel = MelSpectrogramFeatures()(audio).to(self.device) cond_mel_frame = cond_mel.shape[-1] if verbose: print(f"cond_mel shape: {cond_mel.shape}", "dtype:", cond_mel.dtype) self.cache_audio_prompt = audio_prompt self.cache_cond_mel = cond_mel else: cond_mel = self.cache_cond_mel cond_mel_frame = cond_mel.shape[-1] pass auto_conditioning = cond_mel cond_mel_lengths = torch.tensor([cond_mel_frame], device=self.device) # text_tokens text_tokens_list = self.tokenizer.tokenize(text) sentences = self.tokenizer.split_sentences(text_tokens_list, max_tokens_per_sentence=max_text_tokens_per_sentence) if verbose: print(">> text token count:", len(text_tokens_list)) print(" splited sentences count:", len(sentences)) print(" max_text_tokens_per_sentence:", max_text_tokens_per_sentence) print(*sentences, sep="\n") do_sample = generation_kwargs.pop("do_sample", True) top_p = generation_kwargs.pop("top_p", 0.8) top_k = generation_kwargs.pop("top_k", 30) temperature = generation_kwargs.pop("temperature", 1.0) autoregressive_batch_size = 1 length_penalty = generation_kwargs.pop("length_penalty", 0.0) num_beams = generation_kwargs.pop("num_beams", 3) repetition_penalty = generation_kwargs.pop("repetition_penalty", 10.0) max_mel_tokens = generation_kwargs.pop("max_mel_tokens", 600) sampling_rate = 24000 # lang = "EN" # lang = "ZH" wavs = [] gpt_gen_time = 0 gpt_forward_time = 0 bigvgan_time = 0 # text processing all_text_tokens: List[List[torch.Tensor]] = [] self._set_gr_progress(0.1, "text processing...") bucket_max_size = sentences_bucket_max_size if self.device != "cpu" else 1 all_sentences = self.bucket_sentences(sentences, bucket_max_size=bucket_max_size) bucket_count = len(all_sentences) if verbose: print(">> sentences bucket_count:", bucket_count, "bucket sizes:", [(len(s), [t["idx"] for t in s]) for s in all_sentences], "bucket_max_size:", bucket_max_size) for sentences in all_sentences: temp_tokens: List[torch.Tensor] = [] all_text_tokens.append(temp_tokens) for item in sentences: sent = item["sent"] text_tokens = self.tokenizer.convert_tokens_to_ids(sent) text_tokens = torch.tensor(text_tokens, dtype=torch.int32, device=self.device).unsqueeze(0) if verbose: print(text_tokens) print(f"text_tokens shape: {text_tokens.shape}, text_tokens type: {text_tokens.dtype}") # debug tokenizer text_token_syms = self.tokenizer.convert_ids_to_tokens(text_tokens[0].tolist()) print("text_token_syms is same as sentence tokens", text_token_syms == sent) temp_tokens.append(text_tokens) # Sequential processing of bucketing data all_batch_num = sum(len(s) for s in all_sentences) all_batch_codes = [] processed_num = 0 for item_tokens in all_text_tokens: batch_num = len(item_tokens) if batch_num > 1: batch_text_tokens = self.pad_tokens_cat(item_tokens) else: batch_text_tokens = item_tokens[0] processed_num += batch_num # gpt speech self._set_gr_progress(0.2 + 0.3 * processed_num / all_batch_num, f"gpt inference speech... {processed_num}/{all_batch_num}") m_start_time = time.perf_counter() with torch.no_grad(): with torch.amp.autocast(batch_text_tokens.device.type, enabled=self.dtype is not None, dtype=self.dtype): temp_codes = self.gpt.inference_speech(auto_conditioning, batch_text_tokens, cond_mel_lengths=cond_mel_lengths, # text_lengths=text_len, do_sample=do_sample, top_p=top_p, top_k=top_k, temperature=temperature, num_return_sequences=autoregressive_batch_size, length_penalty=length_penalty, num_beams=num_beams, repetition_penalty=repetition_penalty, max_generate_length=max_mel_tokens, **generation_kwargs) all_batch_codes.append(temp_codes) gpt_gen_time += time.perf_counter() - m_start_time # gpt latent self._set_gr_progress(0.5, "gpt inference latents...") all_idxs = [] all_latents = [] has_warned = False for batch_codes, batch_tokens, batch_sentences in zip(all_batch_codes, all_text_tokens, all_sentences): for i in range(batch_codes.shape[0]): codes = batch_codes[i] # [x] if not has_warned and codes[-1] != self.stop_mel_token: warnings.warn( f"WARN: generation stopped due to exceeding `max_mel_tokens` ({max_mel_tokens}). " f"Consider reducing `max_text_tokens_per_sentence`({max_text_tokens_per_sentence}) or increasing `max_mel_tokens`.", category=RuntimeWarning ) has_warned = True codes = codes.unsqueeze(0) # [x] -> [1, x] if verbose: print("codes:", codes.shape) print(codes) codes, code_lens = self.remove_long_silence(codes, silent_token=52, max_consecutive=30) if verbose: print("fix codes:", codes.shape) print(codes) print("code_lens:", code_lens) text_tokens = batch_tokens[i] all_idxs.append(batch_sentences[i]["idx"]) m_start_time = time.perf_counter() with torch.no_grad(): with torch.amp.autocast(text_tokens.device.type, enabled=self.dtype is not None, dtype=self.dtype): latent = \ self.gpt(auto_conditioning, text_tokens, torch.tensor([text_tokens.shape[-1]], device=text_tokens.device), codes, code_lens * self.gpt.mel_length_compression, cond_mel_lengths=torch.tensor([auto_conditioning.shape[-1]], device=text_tokens.device), return_latent=True, clip_inputs=False) gpt_forward_time += time.perf_counter() - m_start_time all_latents.append(latent) del all_batch_codes, all_text_tokens, all_sentences # bigvgan chunk chunk_size = 2 all_latents = [all_latents[all_idxs.index(i)] for i in range(len(all_latents))] if verbose: print(">> all_latents:", len(all_latents)) print(" latents length:", [l.shape[1] for l in all_latents]) chunk_latents = [all_latents[i: i + chunk_size] for i in range(0, len(all_latents), chunk_size)] chunk_length = len(chunk_latents) latent_length = len(all_latents) # bigvgan chunk decode self._set_gr_progress(0.7, "bigvgan decode...") tqdm_progress = tqdm(total=latent_length, desc="bigvgan") for items in chunk_latents: tqdm_progress.update(len(items)) latent = torch.cat(items, dim=1) with torch.no_grad(): with torch.amp.autocast(latent.device.type, enabled=self.dtype is not None, dtype=self.dtype): m_start_time = time.perf_counter() wav, _ = self.bigvgan(latent, auto_conditioning.transpose(1, 2)) bigvgan_time += time.perf_counter() - m_start_time wav = wav.squeeze(1) pass wav = torch.clamp(32767 * wav, -32767.0, 32767.0) wavs.append(wav.cpu()) # to cpu before saving # clear cache tqdm_progress.close() # 确保进度条被关闭 del all_latents, chunk_latents end_time = time.perf_counter() self.torch_empty_cache() # wav audio output self._set_gr_progress(0.9, "save audio...") wav = torch.cat(wavs, dim=1) wav_length = wav.shape[-1] / sampling_rate print(f">> Reference audio length: {cond_mel_frame * 256 / sampling_rate:.2f} seconds") print(f">> gpt_gen_time: {gpt_gen_time:.2f} seconds") print(f">> gpt_forward_time: {gpt_forward_time:.2f} seconds") print(f">> bigvgan_time: {bigvgan_time:.2f} seconds") print(f">> Total fast inference time: {end_time - start_time:.2f} seconds") print(f">> Generated audio length: {wav_length:.2f} seconds") print(f">> [fast] bigvgan chunk_length: {chunk_length}") print(f">> [fast] batch_num: {all_batch_num} bucket_max_size: {bucket_max_size}", f"bucket_count: {bucket_count}" if bucket_max_size > 1 else "") print(f">> [fast] RTF: {(end_time - start_time) / wav_length:.4f}") # save audio wav = wav.cpu() # to cpu if output_path: # 直接保存音频到指定路径中 os.makedirs(os.path.dirname(output_path), exist_ok=True) torchaudio.save(output_path, wav.type(torch.int16), sampling_rate) print(">> wav file saved to:", output_path) return output_path else: # 返回以符合Gradio的格式要求 wav_data = wav.type(torch.int16) wav_data = wav_data.numpy().T return (sampling_rate, wav_data) # 原始推理模式 def infer(self, audio_prompt, text, output_path, verbose=False, max_text_tokens_per_sentence=120, **generation_kwargs): print(">> start inference...") self._set_gr_progress(0, "start inference...") if verbose: print(f"origin text:{text}") start_time = time.perf_counter() # 如果参考音频改变了，才需要重新生成 cond_mel, 提升速度 if self.cache_cond_mel is None or self.cache_audio_prompt != audio_prompt: audio, sr = torchaudio.load(audio_prompt) audio = torch.mean(audio, dim=0, keepdim=True) if audio.shape[0] > 1: audio = audio[0].unsqueeze(0) audio = torchaudio.transforms.Resample(sr, 24000)(audio) cond_mel = MelSpectrogramFeatures()(audio).to(self.device) cond_mel_frame = cond_mel.shape[-1] if verbose: print(f"cond_mel shape: {cond_mel.shape}", "dtype:", cond_mel.dtype) self.cache_audio_prompt = audio_prompt self.cache_cond_mel = cond_mel else: cond_mel = self.cache_cond_mel cond_mel_frame = cond_mel.shape[-1] pass self._set_gr_progress(0.1, "text processing...") auto_conditioning = cond_mel text_tokens_list = self.tokenizer.tokenize(text) sentences = self.tokenizer.split_sentences(text_tokens_list, max_text_tokens_per_sentence) if verbose: print("text token count:", len(text_tokens_list)) print("sentences count:", len(sentences)) print("max_text_tokens_per_sentence:", max_text_tokens_per_sentence) print(*sentences, sep="\n") do_sample = generation_kwargs.pop("do_sample", True) top_p = generation_kwargs.pop("top_p", 0.8) top_k = generation_kwargs.pop("top_k", 30) temperature = generation_kwargs.pop("temperature", 1.0) autoregressive_batch_size = 1 length_penalty = generation_kwargs.pop("length_penalty", 0.0) num_beams = generation_kwargs.pop("num_beams", 3) repetition_penalty = generation_kwargs.pop("repetition_penalty", 10.0) max_mel_tokens = generation_kwargs.pop("max_mel_tokens", 600) sampling_rate = 24000 # lang = "EN" # lang = "ZH" wavs = [] gpt_gen_time = 0 gpt_forward_time = 0 bigvgan_time = 0 progress = 0 has_warned = False for sent in sentences: text_tokens = self.tokenizer.convert_tokens_to_ids(sent) text_tokens = torch.tensor(text_tokens, dtype=torch.int32, device=self.device).unsqueeze(0) # text_tokens = F.pad(text_tokens, (0, 1)) # This may not be necessary. # text_tokens = F.pad(text_tokens, (1, 0), value=0) # text_tokens = F.pad(text_tokens, (0, 1), value=1) if verbose: print(text_tokens) print(f"text_tokens shape: {text_tokens.shape}, text_tokens type: {text_tokens.dtype}") # debug tokenizer text_token_syms = self.tokenizer.convert_ids_to_tokens(text_tokens[0].tolist()) print("text_token_syms is same as sentence tokens", text_token_syms == sent) # text_len = torch.IntTensor([text_tokens.size(1)], device=text_tokens.device) # print(text_len) progress += 1 self._set_gr_progress(0.2 + 0.4 * (progress - 1) / len(sentences), f"gpt inference latent... {progress}/{len(sentences)}") m_start_time = time.perf_counter() with torch.no_grad(): with torch.amp.autocast(text_tokens.device.type, enabled=self.dtype is not None, dtype=self.dtype): codes = self.gpt.inference_speech(auto_conditioning, text_tokens, cond_mel_lengths=torch.tensor([auto_conditioning.shape[-1]], device=text_tokens.device), # text_lengths=text_len, do_sample=do_sample, top_p=top_p, top_k=top_k, temperature=temperature, num_return_sequences=autoregressive_batch_size, length_penalty=length_penalty, num_beams=num_beams, repetition_penalty=repetition_penalty, max_generate_length=max_mel_tokens, **generation_kwargs) gpt_gen_time += time.perf_counter() - m_start_time if not has_warned and (codes[:, -1] != self.stop_mel_token).any(): warnings.warn( f"WARN: generation stopped due to exceeding `max_mel_tokens` ({max_mel_tokens}). " f"Input text tokens: {text_tokens.shape[1]}. " f"Consider reducing `max_text_tokens_per_sentence`({max_text_tokens_per_sentence}) or increasing `max_mel_tokens`.", category=RuntimeWarning ) has_warned = True code_lens = torch.tensor([codes.shape[-1]], device=codes.device, dtype=codes.dtype) if verbose: print(codes, type(codes)) print(f"codes shape: {codes.shape}, codes type: {codes.dtype}") print(f"code len: {code_lens}") # remove ultra-long silence if exits # temporarily fix the long silence bug. codes, code_lens = self.remove_long_silence(codes, silent_token=52, max_consecutive=30) if verbose: print(codes, type(codes)) print(f"fix codes shape: {codes.shape}, codes type: {codes.dtype}") print(f"code len: {code_lens}") self._set_gr_progress(0.2 + 0.4 * progress / len(sentences), f"gpt inference speech... {progress}/{len(sentences)}") m_start_time = time.perf_counter() # latent, text_lens_out, code_lens_out = \ with torch.amp.autocast(text_tokens.device.type, enabled=self.dtype is not None, dtype=self.dtype): latent = \ self.gpt(auto_conditioning, text_tokens, torch.tensor([text_tokens.shape[-1]], device=text_tokens.device), codes, code_lens * self.gpt.mel_length_compression, cond_mel_lengths=torch.tensor([auto_conditioning.shape[-1]], device=text_tokens.device), return_latent=True, clip_inputs=False) gpt_forward_time += time.perf_counter() - m_start_time m_start_time = time.perf_counter() wav, _ = self.bigvgan(latent, auto_conditioning.transpose(1, 2)) bigvgan_time += time.perf_counter() - m_start_time wav = wav.squeeze(1) wav = torch.clamp(32767 * wav, -32767.0, 32767.0) if verbose: print(f"wav shape: {wav.shape}", "min:", wav.min(), "max:", wav.max()) # wavs.append(wav[:, :-512]) wavs.append(wav.cpu()) # to cpu before saving end_time = time.perf_counter() self._set_gr_progress(0.9, "save audio...") wav = torch.cat(wavs, dim=1) wav_length = wav.shape[-1] / sampling_rate print(f">> Reference audio length: {cond_mel_frame * 256 / sampling_rate:.2f} seconds") print(f">> gpt_gen_time: {gpt_gen_time:.2f} seconds") print(f">> gpt_forward_time: {gpt_forward_time:.2f} seconds") print(f">> bigvgan_time: {bigvgan_time:.2f} seconds") print(f">> Total inference time: {end_time - start_time:.2f} seconds") print(f">> Generated audio length: {wav_length:.2f} seconds") print(f">> RTF: {(end_time - start_time) / wav_length:.4f}") # save audio wav = wav.cpu() # to cpu if output_path: # 直接保存音频到指定路径中 if os.path.isfile(output_path): os.remove(output_path) print(">> remove old wav file:", output_path) if os.path.dirname(output_path) != "": os.makedirs(os.path.dirname(output_path), exist_ok=True) torchaudio.save(output_path, wav.type(torch.int16), sampling_rate) print(">> wav file saved to:", output_path) return output_path else: # 返回以符合Gradio的格式要求 wav_data = wav.type(torch.int16) wav_data = wav_data.numpy().T return (sampling_rate, wav_data) if __name__ == "__main__": prompt_wav = "examples/voice_01.wav" text = '欢迎大家来体验indextts2，并给予我们意见与反馈，谢谢大家。' tts = IndexTTS(cfg_path="checkpoints/config.yaml", model_dir="checkpoints", use_cuda_kernel=False) tts.infer(audio_prompt=prompt_wav, text=text, output_path="gen.wav", verbose=True)